This invention relates to the field of image detection and more specifically to the detection of a face disposed within a digital image.
As broadcasting becomes more digitally based, it becomes easier to archive and catalog video content. Researchers have developed systems for content-based image and video indexing and retrieval that utilize low-level visual features (semantics) like color, texture, shape and sketch of an image. To facilitate the automatic archiving and retrieval of video material based on higher level semantics, it is important to detect and recognize events in video clips. Human activities are important events in video clips and face detection is a step toward the recognization of human activities.
Face detection is also useful in security systems, criminal identifications, digital image capturing, and teleconferences. In a security system, for example, it is useful to detect the facial portions of an image being viewed so that an operator of the system can discern whether a human is present in the image.
The detection of faces from images has not received much attention by researchers. Most conventional techniques concentrate on face recognition and assume that a face has been identified within the image or assume that the image only has one face as in a "mug shot" image. Such conventional techniques are unable to detect faces from complex backgrounds.
One prior art technique that does perform face detection determines whether a cluster of pixels conforms to a facial template. This routine is deficient because of different scales and orientations of possible faces. The template itself is one size and orientation and will not detect faces which are of a different size or are rotated. Consequently, the template itself must be scaled up and down and rotated while searching is performed yielding a search space that is too big to be useful or practical. Some prior art techniques, like EPA 0836326 A2, use merely a shape template to see if a cluster of pixels conforms to that shape. In addition to the scaling and rotation problems mentioned above, this solution is too simplistic to be used with complex backgrounds which may have many objects with the same shape as a face and perhaps even the same color as a face.
Therefore, there exists a need for a method of detecting faces within a digital image in which the face is disposed within a complex background.